Deep Learning is gaining popularity among Artificial intelligence group and data scientist group. It has become of the love of data lovers across the globe. Recently, researchers have started to question that deep learning is the future of Artificial intelligence.

The deep learning methods used now all rely on supervised learning, we see quite apparent that individuals learn things, patterns, and thoughts without much supervision at all. In a sense, our knowledge is unsupervised. Unsupervised learning helps to find all the unknown trends and design in the data. Where unsupervised learning is clearly behind supervised learning because of some clear reasons because not much work is done under this topic, also the model under unsupervised learning is quite challenging to define. These techniques might consist of clues for the future of artificial intelligence research. In this post, we're going to discover about unsupervised learning techniques and their application.

What is unsupervised learning?

Unsupervised learning is a machine learning technique. The main goal of unsupervised learning is to allow the model and to discover insights and patterns on its own. It's known as unsupervised because there is no specific answer and no teacher. The model has to discover and find all the unknown trends in the underlying data. Some of the unsupervised learning methods that work quite great in specific applications and environments. Unsupervised learning recognises commonalities in the underlying data and reacts based on the presence of any such commonalities in the dataset. unsupervised learning can also be random when compared with additional natural learning methods.

For example, let's consider a baby and its pet cat. In a few weeks, the baby identifies all the features two eyes, ears nose and play with the cat. A few days later a family friend visit and gets his cat and the baby was able to recognize that this pet is similar to his pet due to similar features but knew that cat was different from his cat. This is a clear example that it learns and discover the structure by itself. 

Why implement Unsupervised Learning

We need to implement unsupervised learning techniques because:-

  • Unsupervised learning algorithm helps to find all kinds of trends and unknown pattern in the underlying data. 

  • the algorithm helps to discover all kinds of features that could be useful for categorization.

  • The input data is analyzed and labelled in real-time in presence of learners.

  • It is always easy to get unstructured and unlabeled data than the labelled as it needs manual intervention.

  • The most suitable time to apply unsupervised machine learning is when we don’t have data for aspired results, like identifying a target market for a completely new product in our business.

Types of Unsupervised Learning 


Clustering deals in searching for the pattern and trends of categorized data. These algorithms process the data and find clusters or groups of same features.

Groups of data are formed by using a clustering algorithm in so that the data points can be classified into specific groups. All the data points have somewhat the same properties, and features in the same group, whereas different groups have unique properties or characteristics.

The similarity between these points is quantified by a distance metric based on the feature variable set. There are various types of Clustering we can use:

  • K-Means Clustering – clustering your data points within a k-number of mutually exclusive clusters. A lot of complexity encompasses how to select the right number (K).

  • Hierarchical Clustering – a technique used to cluster the data points into the origin and minor clusters. You might split your consumers between fresher and more experienced ones, and then divide all of these groups into individual clusters.

  • Probabilistic Clustering – a technique that is used to cluster all the data points using a probabilistic scale. 

Clustering techniques are an easy yet powerful technique as they require intensive effort to give us a very relevant insight from our data. In that capacity, it's been utilized in numerous applications for a considerable length of time including  

-Science, for hereditary and species gathering 

-Therapeutic imaging, for recognizing various types of tissue 

-Statistical surveying, for understanding the multiple collections of organizations and clients dependent on individual traits 

-Proposal frameworks, for example, giving you better Amazon recommendations 


In machine learning, we may find certain conditions where feature representations are just too huge to handle. Autoenodes is a technique in which we use neural networks for the image.

For example, we are using face recognition application and would like to save all the templates of a person's face in our data warehouse so that it can be used for future reference. To keep the colour image of the person of 168x168,  we would have to keep 168 * 168 * 3  float values for each face! Similarly, understand in order this large file for one person and then for more than 1000 people, the amount of space use would be huge. So this where Autoencoders come into the picture. With an autoencoder, we can encoder features that helps to take less space but still, efficiently represent the same thing.

So to work this out, we will have to train a neural network to predict its input. Here there is a small catch, i.e. the middle layer of our autoencoder has fewer features than input and output. To train our neural network to learn a compressed version of our feature representation.once this method is applied all the files are compressed, and they're reducing the size of data.   


Expectation-Maximization (EM) algorithms are a set of iterative methods meant to determine the parameters for several statistical models to explicitly model data. For example, that the data is Gaussian distributed as in the graphic here and we need to find the best parameters for a Gaussian to model it. An Expectation-Maximization algorithm allows us to automatically determine the Gaussian parameters like mean and variation in every direction

In an EM algorithm, we shift within the Expectations(E) step and a Maximization (M) step. The E step uses the current parameters to create our statistical model and applies it to our data.

Difficulties in Executing Unsupervised Learning

A big question for many researchers today is "unsupervised learning work for me?" question is dependent on your business context. In our case of customer segmentation, Clustering will only serve well if your consumers do fit into common groups. One of the best ways to test your unsupervised learning model is by completing it in the real world and seeing what happens! Creating a C0/B0 test including and not including the clusters your algorithm outputted can be an efficient way to identify if it's valuable knowledge or inaccurate. Researchers should also be striving on algorithms that might give a more accurate performance in unsupervised learning.

Category : Data Collection
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